You can export these graphs, using tf.saved_model, to run on other systems like a server or a mobile device, no Python installation required. Read the tensor slicing guide to learn how you can apply indexing to manipulate individual elements in your tensors. Manipulating Shapes Inverse Document Frequency: Mainly, it tests how relevant the word is. The key aim of the search is to locate the appropriate records that fit the demand. Since tf considers all terms equally significant, it is therefore not only possible to use the term frequencies to measure the weight of the term in the paper. First, find the document frequency of a term t by counting the number of documents containing the term: Unlike a mathematical op, for example, broadcast_to does nothing special to save memory. Here, you are materializing the tensor. The derivative of y is y' = f'(x) = (2*x + 2) = 4. TensorFlow can calculate this automatically: with tf.GradientTape() as tape:

That's working, but remember that implementations of common training utilities are available in the tf.keras module. So, consider using those before writing your own. To start with, the Model.compile and Model.fit methods implement a training loop for you: While you can use TensorFlow interactively like any Python library, TensorFlow also provides tools for:Passing an integer for each index, the result is a scalar. # Pull out a single value from a 2-rank tensor tf.string is a dtype, which is to say you can represent data as strings (variable-length byte arrays) in tensors. The strings are atomic and cannot be indexed the way Python strings are. The length of the string is not one of the axes of the tensor. See tf.strings for functions to manipulate them. Given that this model is intended to predict continuous values, the mean squared error (MSE) is a good choice for the loss function. Given a vector of predictions, \(\hat{y}\), and a vector of true targets, \(y\), the MSE is defined as the mean of the squared differences between the predicted values and the ground truth.

Typically the only reasonable use of tf.reshape is to combine or split adjacent axes (or add/remove 1s). You may run across not-fully-specified shapes. Either the shape contains a None (an axis-length is unknown) or the whole shape is None (the rank of the tensor is unknown). E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered TensorFlow implements standard mathematical operations on tensors, as well as many operations specialized for machine learning. E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registeredAll tensors are immutable like Python numbers and strings: you can never update the contents of a tensor, only create a new one. Basics Now, observe your model's performance after training: plot_preds(x, y, f, quad_model, 'After training') Here is a "scalar" or "rank-0" tensor . A scalar contains a single value, and no "axes". # This will be an int32 tensor by default; see "dtypes" below. You can import and export the tf.Variable values and the tf.function graphs using tf.saved_model. This allows you to run your model independently of the Python program that created it. You begin with '' Look at the lowest row which is the 5M TF. If the bar directly above the 5M bar is also blue you take the trade long''

Meaning also blue with what? Refer to the doc attached. It has to have the 2 lowest TF same colour to enter the trade? Please restate if you can the rules for entering. Please note no SLs were placed on any of these trades, if you wanted to place a SL at the most recent high/low you would have had only a few instances where they were hit. See last two pictures. This affects only type 2 entries. Tensors are multi-dimensional arrays with a uniform type (called a dtype). You can see all supported dtypes at tf.dtypes. But note that the Tensor.ndim and Tensor.shape attributes don't return Tensor objects. If you need a Tensor use the tf.rank or tf.shape function. This difference is subtle, but it can be important when building graphs (later). tf.rank(rank_4_tensor)You see what broadcasting looks like using tf.broadcast_to. print(tf.broadcast_to(tf.constant([1, 2, 3]), [3, 3])) The weight of a term that occurs in a document is simply proportional to the term frequency. tf(t,d) = count of t in d / number of words in d You can reshape a tensor into a new shape. The tf.reshape operation is fast and cheap as the underlying data does not need to be duplicated. # You can reshape a tensor to a new shape. A graph may not be reusable for inputs with a different signature ( shape and dtype), so a new graph is generated instead: x = tf.constant([10.0, 9.1, 8.2], dtype=tf.float32)